The rise of antimicrobial drug resistance persists as bacteria continue to evolve and new infectious organisms are encountered. This situation has created an imperative need to develop new compounds to treat these infections both now and in the future. Metabolism represents a potentially rich source of targets for future antimicrobial drug development that remains untapped. The ability to identify metabolic targets and understand their essential roles at a high level of resolution from a mechanistic perspective is ideal for the development of therapeutics. Taking advantage of the inherent similarities and differences in the metabolic network components and functional capabilities of metabolism across many organisms may offer an unprecedented ability to design narrow as well as broad spectrum antibiotics, provided the necessary computational/experimental infrastructure. Herein we put forth an SBIR program that takes the initial first steps toward building a model-driven technology platform capable of generating the metabolic insight and characterization necessary to exploit this potentially rich class of targets. We will be using established approaches from constraints-based modeling and the powerful capabilities of SimPheny to perform modeling and integrative analysis of experimental data. Through the enhancement of an existing model of H. influenzae within SimPheny and the work proposed herein this program will generate: 1) an improved characterization of H. influenzae metabolic pathways and their regulation and role in invasive disease; 2) the necessary representation of metabolic systems to provide the context for integrative analysis of high throughput data sets (i.e. gene/protein expression profiles); 3) a scalable platform to demonstrate the capability of using models to design experimental plans and guide biological discovery; 4) novel targets for the development of antimicrobials for H. influenzae infections; 5) and enhanced validation of existing targets in H. influenzae.